Upload convert_url_to_diffusers_multi_gr.py

convert_url_to_diffusers_multi_gr.py

@@ -293,7 +293,7 @@ def load_and_save_pipeline(pipe, model_type: str, url: str, new_file: str, new_d
             pipe.save_pretrained(new_dir)
     except Exception as e:
         print(f"Failed to load pipeline. {e}")
-        raise Exception("Failed to load pipeline.") from e
+        raise Exception(f"Failed to load pipeline. {e}") from e
     finally:
         return pipe
 
@@ -305,20 +305,22 @@ def convert_url_to_diffusers(url: str, civitai_key: str="", is_upload_sf: bool=F
         hf_token = get_token()
         progress(0, desc="Start converting...")
         temp_dir = TEMP_DIR
+        pipe = None
 
         if is_repo_name(url) and is_repo_exists(url):
             new_file = url
             model_type = mtype
         else:
             new_file = get_download_file(temp_dir, url, civitai_key)
-            if not new_file or Path(new_file).suffix not in set([".safetensors", ".ckpt", ".bin", ".sft"]): raise Exception(f"Safetensors file not found: {url}")
+            if not new_file or Path(new_file).suffix.lower() not in set([".safetensors", ".ckpt", ".bin", ".sft"]):
+                safe_clean(new_file)
+                raise Exception(f"Safetensors file not found: {url}")
             model_type = get_model_type_from_key(new_file)
         new_dir = Path(new_file).stem.replace(" ", "_").replace(",", "_").replace(".", "_") #
         
         kwargs = {}
         dkwargs = {}
         if dtype != DTYPE_DEFAULT: dkwargs["torch_dtype"] = get_process_dtype(dtype, model_type)
-        pipe = None
 
         print(f"Model type: {model_type} / VAE: {vae} / CLIP: {clip} / T5: {t5} / Scheduler: {scheduler} / dtype: {dtype} / EMA: {ema} / Base repo: {base_repo} / LoRAs: {lora_dict}")
 
@@ -387,7 +389,7 @@ def convert_url_to_diffusers(url: str, civitai_key: str="", is_upload_sf: bool=F
         return new_dir
     except Exception as e:
         print(f"Failed to convert. {e}")
-        raise Exception("Failed to convert.") from e
+        raise Exception(f"Failed to convert. {e}") from e
     finally:
         del pipe
         torch.cuda.empty_cache()